通过科学机器学习和转移学习来预测香水的气味值的框架
Luis M C Oliveira1,2, Vinícius V Santana1,2, Alírio E Rodrigues1,2
1LSRE-LCM - Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal.
Heliyon
|October 23, 2023
概括
这项研究引入了一种机器学习方法,用于预测气味值,这对香水行业至关重要. 与传统方法相比,转移学习模型显示出更高的准确性,为气味分析提供了宝贵的工具.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 感官科学是一种感官科学.
背景情况:
- 气味值对于香水行业至关重要,但很难衡量.
- 经验模型可以从分子结构中估计气味值.
- 科学机器学习提供了新的预测策略.
研究的目的:
- 开发和评估用于预测化学气味值的机器学习框架.
- 为了利用转移学习来提高气味值预测的准确性.
- 将拟议的模型与基准方法和现有的相关性进行比较.
主要方法:
- 一个转移学习策略,结合了图形卷积网络 (GCN) 和前神经网络 (FNN).
- GCNs预测了语义气味描述符,输出作为FNN的输入.
- 根据分子结构,FNN估计了气味值.
主要成果:
- 基于转移学习的模型显著优于缺乏转移学习的基准模型.
- 提出的方法显示出比文献相关性和假回归器更好的预测性能.
- 这表明转移学习在气味值预测中的有效性.
结论:
- 转移学习为预测气味值提供了一种强大而准确的方法.
- 开发的框架为香水和香水行业提供了有价值的工具.
- 这种方法有助于从分子信息中估计气味特性.
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